In this work, we implement the CGCNN[1]} architecture as the GNN encoder. We choose CGCNN because of its competitive performance and computational efficiency when compared to other GNN baselines. To encode crystal features and obtain an embedding, we use mean pooling to generate a latent representation with the dimension of 64. To expand the latent embedding and have a better combination of encoded features, we use a projector with 2 MLP layers that generates a final embedding of size 128. We use this embedding to generate the cross-correlation matrix for applying the Barlow Twins loss.
